{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Starting comments\n",
    "Charles Le Losq, Created 7 April 2015 for Python, Modified 30 Sept. 2016 for Julia\n",
    "\n",
    "This IJulia notebook is aimed to show how you can easily fit a Raman spectrum with Julia , for free and, in my opinion, in an elegant way. Julia presents some advantages over Python (speed, in my opinion more simplicity as it is directly made for scientific computing) and inconvenients (young, so may have breakups, not that much libraries). But it is definitely worth the try. For optimisation I think the JuMP package does a really good job, leaving you lots of choice for your optimisation setup and algorithm.\n",
    "\n",
    "The following fitting procedure phylosophie is totally in contradiction with most existing GUI softwares. It probably is a little bit harder to learn for the newcomer, but you have full control over the procedure.\n",
    "\n",
    "In this example, we will fit the 850-1300 cm$^{-1}$ portion of a Raman spectrum of a lithium tetrasilicate glass Li$_2$Si$_4$O$_9$, the name will be abbreviated LS4 in the following. \n",
    "\n",
    "For further references for fitting Raman spectra of glasses, please see for instance: Virgo et al., 1980, Science 208, p 1371-1373; Mysen et al., 1982, American Mineralogist 67, p 686-695; McMillan, 1984, American Mineralogist 69, p 622-644; Mysen, 1990, American Mineralogist 75, p 120-134; Le Losq et al., 2014, Geochimica et Cosmochimica Acta 126, p 495-517 and Le Losq et al., 2015, Progress in Earth and Planetary Sciences 2:22.\n",
    "\n",
    "We will use the optimization algorithms of Ipopt with JuMP. Please consult http://www.juliaopt.org/ for further details."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Importing libraries\n",
    "So the first part will be to import a bunch of libraries for doing various things (quite straightforward with Julia)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO: Precompiling module Spectra.\n",
      "WARNING: Base.WORD_SIZE is deprecated.\n",
      "  likely near /Users/charles/.julia/v0.5/Spectra/src/Spectra.jl:39\n",
      "INFO: Recompiling stale cache file /Users/charles/.julia/lib/v0.5/FileIO.ji for module FileIO.\n",
      "INFO: Recompiling stale cache file /Users/charles/.julia/lib/v0.5/JLD.ji for module JLD.\n"
     ]
    }
   ],
   "source": [
    "using JuMP\n",
    "using PyPlot\n",
    "using Ipopt\n",
    "using Spectra\n",
    "using JLD"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Importing and looking at the data\n",
    "Let's first have a look at the spectrum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x32b23bd10>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "PyObject <matplotlib.text.Text object at 0x32bef7390>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get the spectrum to deconvolute, with skipping header and footer comment lines from the spectrometer\n",
    "data = readdlm(\"./data/LS4.txt\", '\\t')\n",
    "\n",
    "# To skip header and footer lines\n",
    "skip_header = 23\n",
    "skip_footer = 121\n",
    "inputsp = zeros(size(data)[1]-skip_header-skip_footer,2)\n",
    "j = 1\n",
    "for i = skip_header+1:size(data)[1]-skip_footer\n",
    "    inputsp[j,1] = Float64(data[i,1])\n",
    "    inputsp[j,2] = Float64(data[i,2])\n",
    "    j = j + 1\n",
    "end\n",
    "\n",
    "# performing the long correction; not always necessary at frequencies > 500 cm-1, \n",
    "# but this is just for the sack of example in the present case\n",
    "inputsp[:,1], inputsp[:,2],~ = tlcorrection(inputsp,23.0,490.0)\n",
    "\n",
    "# create a new plot for showing the spectrum\n",
    "figure()\n",
    "\n",
    "plot(inputsp[:,1],inputsp[:,2],color=\"black\")\n",
    "\n",
    "xlabel(L\"Raman shift, cm$^{-1}$\", fontsize = 14)\n",
    "ylabel(\"Normalized intensity, a. u.\", fontsize = 14)\n",
    "title(\"Figure 1: the spectrum of interest\",fontsize = 14, fontweight = \"bold\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So we are looking at the 500-1300 cm$^{-1}$ portion of the Raman spectrum of the glass. We see a peak near 800 cm$^{-1}$, and two others near 950 and 1085 cm$^{-1}$. We will be interested in fitting the 870-1300 cm$^{-1}$ portion of this spectrum, which can be assigned to the various symmetric and assymetric stretching vibrations of Si-O bonds in the SiO$_2$ tetrahedra present in the glass network (see the above cited litterature for details).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Baseline Removal\n",
    "\n",
    "First thing we notice in Fig. 1, we have to remove a baseline because this spectrum is shifted from 0 by some \"background\" scattering. This quite typical in Raman spectra of glasses. Several ways exist to do so. We're going to the simplest thing: a polynomial fitting the signal base around 870 and 1300 cm$^{-1}$. Other reasonnable solutions include a linear function, and a constant function. The two latter can be fitted between 1300 and 1350 cm$^{-1}$, but we will need to add another peak around 800 cm$^{-1}$. For now, the example is done with fitting the 870 cm$^{-1}$ portion of spectra, as this usually results in more robust final results.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x32e9f3b10>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: Base.writemime is deprecated.\n",
      "  likely near /Users/charles/.julia/v0.5/IJulia/src/kernel.jl:31\n",
      "WARNING: Base.writemime is deprecated.\n",
      "  likely near /Users/charles/.julia/v0.5/IJulia/src/kernel.jl:31\n",
      "WARNING: Base.writemime is deprecated.\n",
      "  likely near /Users/charles/.julia/v0.5/IJulia/src/kernel.jl:31\n",
      "WARNING: Base.writemime is deprecated.\n",
      "  likely near /Users/charles/.julia/v0.5/IJulia/src/kernel.jl:31\n",
      "WARNING: Base.writemime is deprecated.\n",
      "  likely near /Users/charles/.julia/v0.5/IJulia/src/kernel.jl:31\n",
      "WARNING: Base.writemime is deprecated.\n",
      "  likely near /Users/charles/.julia/v0.5/IJulia/src/kernel.jl:31\n",
      "WARNING: Base.writemime is deprecated.\n",
      "  likely near /Users/charles/.julia/v0.5/IJulia/src/kernel.jl:31\n",
      "WARNING: Base.writemime is deprecated.\n",
      "  likely near /Users/charles/.julia/v0.5/IJulia/src/kernel.jl:31\n",
      "WARNING: Base.writemime is deprecated.\n",
      "  likely near /Users/charles/.julia/v0.5/IJulia/src/kernel.jl:31\n",
      "WARNING: Base.writemime is deprecated.\n",
      "  likely near /Users/charles/.julia/v0.5/IJulia/src/kernel.jl:31\n",
      "WARNING: Base.writemime is deprecated.\n",
      "  likely near /Users/charles/.julia/v0.5/IJulia/src/kernel.jl:31\n",
      "WARNING: Base.writemime is deprecated.\n",
      "  likely near /Users/charles/.julia/v0.5/IJulia/src/kernel.jl:31\n",
      "WARNING: Base.writemime is deprecated.\n",
      "  likely near /Users/charles/.julia/v0.5/IJulia/src/kernel.jl:31\n",
      "in show at /Users/charles/.julia/v0.5/PyCall/src/PyCall.jl\n",
      "WARNING: Base.writemime is deprecated.\n",
      "  likely near /Users/charles/.julia/v0.5/IJulia/src/kernel.jl:31\n",
      "in show at /Users/charles/.julia/v0.5/PyCall/src/PyCall.jl\n",
      "WARNING: Base.writemime is deprecated.\n",
      "  likely near /Users/charles/.julia/v0.5/IJulia/src/kernel.jl:31\n",
      "in show at /Users/charles/.julia/v0.5/PyCall/src/PyCall.jl\n",
      "WARNING: Base.writemime is deprecated.\n",
      "  likely near /Users/charles/.julia/v0.5/IJulia/src/kernel.jl:31\n",
      "in show at /Users/charles/.julia/v0.5/PyCall/src/PyCall.jl\n",
      "WARNING: Base.writemime is deprecated.\n",
      "  likely near /Users/charles/.julia/v0.5/IJulia/src/kernel.jl:31\n",
      "in show at /Users/charles/.julia/v0.5/PyCall/src/PyCall.jl\n",
      "WARNING: Base.writemime is deprecated.\n",
      "  likely near /Users/charles/.julia/v0.5/IJulia/src/kernel.jl:31\n",
      "in show at /Users/charles/.julia/v0.5/PyCall/src/PyCall.jl\n",
      "WARNING: Base.writemime is deprecated.\n",
      "  likely near /Users/charles/.julia/v0.5/IJulia/src/kernel.jl:31\n",
      "in show at /Users/charles/.julia/v0.5/PyCall/src/PyCall.jl\n",
      "WARNING: Base.writemime is deprecated.\n",
      "  likely near /Users/charles/.julia/v0.5/IJulia/src/kernel.jl:31\n",
      "in show at /Users/charles/.julia/v0.5/PyCall/src/PyCall.jl\n",
      "WARNING: Base.writemime is deprecated.\n",
      "  likely near /Users/charles/.julia/v0.5/IJulia/src/kernel.jl:31\n",
      "in show at /Users/charles/.julia/v0.5/PyCall/src/PyCall.jl\n",
      "WARNING: Base.writemime is deprecated.\n",
      "  likely near /Users/charles/.julia/v0.5/IJulia/src/kernel.jl:31\n",
      "in show at /Users/charles/.julia/v0.5/PyCall/src/PyCall.jl\n",
      "WARNING: Base.writemime is deprecated.\n",
      "  likely near /Users/charles/.julia/v0.5/IJulia/src/kernel.jl:31\n",
      "in show at /Users/charles/.julia/v0.5/PyCall/src/PyCall.jl\n",
      "WARNING: Base.writemime is deprecated.\n",
      "  likely near /Users/charles/.julia/v0.5/IJulia/src/kernel.jl:31\n",
      "in show at /Users/charles/.julia/v0.5/PyCall/src/PyCall.jl\n",
      "WARNING: New baseline version requires the revision of old smoothing p values for splines.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "PyObject <matplotlib.legend.Legend object at 0x32ec2a850>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# the regions of interest roi\n",
    "roi = [860.0 870.0; 1300.0 1400.0]\n",
    "\n",
    "\n",
    "y_corr, y_bas, xy_roi = baseline(inputsp[:,1],inputsp[:,2],roi,\"gcvspline\",p=0.5,roi_out = \"yes\") \n",
    "\n",
    "#Creates a plot showing the baseline\n",
    "figure()\n",
    "plot(inputsp[:,1],y_corr[:,1],color=\"black\",label=\"Raw data\")\n",
    "scatter(xy_roi[:,1],xy_roi[:,2],color=\"red\",label=\"ROI\")\n",
    "plot(inputsp[:,1],y_bas[:,1],color=\"blue\",label=\"Baseline\")\n",
    "plot(inputsp[:,1],inputsp[:,2],color=\"green\",label=\"Treated sp.\")\n",
    "\n",
    "title(\"Figure 2: the fit of the background\",fontsize = 18, fontweight = \"bold\")\n",
    "\n",
    "# we set the values of the labels of the x and y axis.\n",
    "xlabel(L\"Raman shift, cm$^{-1}$\",fontsize=18) # The L in front of the string indicates that we use Latex for the maths\n",
    "ylabel(\"Intensity, a. u.\",fontsize=18)\n",
    "\n",
    "# we display the legend at the best location, without a frame\n",
    "legend(loc=\"best\",frameon=false)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we will do some manipulation to have the interested portion of spectrum in a single variable. We will assume that the errors have not been drastically affected by the correction process (in some case it can be, but this one is quite straightforward), such that we will use the initial relative errors stored in the \"ese0\" variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done\n"
     ]
    }
   ],
   "source": [
    "index_interest = find(867.0 .< inputsp[:,1] .< 1300.0)\n",
    "\n",
    "interestspectra = y_corr[index_interest,1]\n",
    "ese0 = sqrt(abs(interestspectra[:,1]))/abs(interestspectra[:,1]) # the relative errors after baseline subtraction\n",
    "interestspectra[:,1] = interestspectra[:,1]/trapz(inputsp[index_interest,1],interestspectra[:,1])*1000. # normalise spectra to maximum intensity, easier to handle \n",
    "\n",
    "# First we simplify things by calling x, y and the frequency and intensity of spectra for later use\n",
    "sigma = abs(ese0.*interestspectra[:,1]) #calculate good ese\n",
    "x = inputsp[index_interest,1]\n",
    "y = interestspectra[:,1]\n",
    "println(\"Done\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fitting the spectrum\n",
    "\n",
    "All the fitting will be done using JuMP, and the Ipopt solver. Good thing, you can change the solver as you want. JuMP is just a way to express things (a little bit like lmfit under python, but much more flexible).\n",
    "\n",
    "There was a long speach at this point in the Python version of this notebook, but Julia allows to fit very easily the spectrum. It is quite obvious in the following lines that we create a model, we define the variables containing the peaks amplitudes, frequency and widths (hwhm), and we set them. \n",
    "\n",
    "If more or less peaks are needed, simply change the number of peaks (variable m), and adjust the initial parameters after the setValue function calls.\n",
    "\n",
    "Constraints are possible to implement too, quite easily. Usually Ipopt gives a very good results without needing constraints."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Constructed\n"
     ]
    }
   ],
   "source": [
    "#Change the following parameters to adjust the model\n",
    "amp_guess = [1,1,1,1,1]\n",
    "freq_guess = [950,1050,1090,1140,1190]\n",
    "hwhm_guess = [30,30,30,30,30]\n",
    "\n",
    "# we need to know the number of data points as well as the number of peaks\n",
    "n = size(x,1) # number of data\n",
    "m = 5 #number of peaks, to be modified!\n",
    "\n",
    "# The model for fitting baseline to roi signal\n",
    "mod = Model(solver=IpoptSolver())\n",
    "#mod = Model(solver=MosekSolver())\n",
    "# we declare three variable arrays that contain the amplitudes, frequencies and width of peaks\n",
    "@variable(mod,g_amplitudes[i=1:m] >= 0.0) # we can add constrain during declaration: the amplitude cannot be negative\n",
    "@variable(mod,850 <= g_frequency[i=1:m] <= 1250)\n",
    "@variable(mod,20.0 <= g_hwhm[i=1:m] <= 50) # and we retrain the width to a reasonnable amount\n",
    "\n",
    "# setting initial values\n",
    "setvalue(g_amplitudes[i=1:m],amp_guess[i])\n",
    "setvalue(g_frequency[i=1:m],freq_guess[i])\n",
    "setvalue(g_hwhm[i=1:m],hwhm_guess[i])\n",
    "\n",
    "# add constraint\n",
    "@NLconstraint(mod, freq_g1[j=1:n], 850 <= g_frequency[1] <= 980)\n",
    "@NLconstraint(mod, freq_g2[j=1:n], 980 <= g_frequency[2] <= 1065)\n",
    "@NLconstraint(mod, freq_g3[j=1:n], 1065 <= g_frequency[3] <= 1110)\n",
    "@NLconstraint(mod, freq_g4[j=1:n], 1110 <= g_frequency[4] <= 1170)\n",
    "@NLconstraint(mod, freq_g5[j=1:n], 1170 <= g_frequency[5] <= 1250)\n",
    "@NLconstraint(mod, hwhm_g5[j=1:n], 20 <= g_hwhm[5] <= 35)\n",
    "\n",
    "# here we write the function of our model that allows the direct calculation\n",
    "@NLexpression(mod,g_mod[j=1:n],sum{exp(-log(2) * ((x[j]-g_frequency[i])/g_hwhm[i])^2), i = 1:m})\n",
    "\n",
    "# Then we write the objective function\n",
    "@NLobjective(mod,Min,sum{(g_mod[j] - y[j])^2, j=1:n})\n",
    "println(\"Constructed\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And below, we can launch the fitting procedure:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Solving...\n",
      "This is Ipopt version 3.12.4, running with linear solver mumps.\n",
      "NOTE: Other linear solvers might be more efficient (see Ipopt documentation).\n",
      "\n",
      "Number of nonzeros in equality constraint Jacobian...:        0\n",
      "Number of nonzeros in inequality constraint Jacobian.:    12990\n",
      "Number of nonzeros in Lagrangian Hessian.............:       55\n",
      "\n",
      "Total number of variables............................:       15\n",
      "                     variables with only lower bounds:        5\n",
      "                variables with lower and upper bounds:       10\n",
      "                     variables with only upper bounds:        0\n",
      "Total number of equality constraints.................:        0\n",
      "Total number of inequality constraints...............:    12990\n",
      "        inequality constraints with only lower bounds:        0\n",
      "   inequality constraints with lower and upper bounds:    12990\n",
      "        inequality constraints with only upper bounds:        0\n",
      "\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "   0  8.4295421e+02 0.00e+00 1.83e+01  -1.0 0.00e+00    -  0.00e+00 0.00e+00   0\n",
      "   1  5.7574051e+02 0.00e+00 1.17e+01  -1.0 4.15e+01    -  3.23e-01 2.39e-01f  1\n",
      "   2  5.1125710e+02 0.00e+00 7.18e+00  -1.0 1.12e+01    -  6.26e-01 2.41e-01f  1\n",
      "   3  4.4843982e+02 0.00e+00 2.42e+00  -1.0 1.47e+01    -  9.25e-01 5.47e-01f  1\n",
      "   4  4.3220805e+02 0.00e+00 8.92e-01  -1.0 5.28e+00    -  9.90e-01 3.74e-01f  1\n",
      "   5  3.8384040e+02 0.00e+00 3.44e-01  -1.7 1.06e+02    -  9.91e-01 9.98e-01f  1\n",
      "   6  3.4036486e+02 0.00e+00 3.01e-01  -1.7 1.06e+04    -  1.00e+00 1.00e+00f  1\n",
      "   7  3.4544672e+02 0.00e+00 1.55e-02  -1.7 8.93e+04    -  1.00e+00 1.00e+00f  1\n",
      "   8  3.2654024e+02 0.00e+00 6.46e-02  -3.8 7.62e+00    -  8.50e-01 7.83e-01f  1\n",
      "   9  3.0448156e+02 0.00e+00 1.62e-01  -3.8 1.75e+01    -  8.53e-01 8.49e-01f  1\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "  10  2.9830506e+02 0.00e+00 7.29e-02  -3.8 7.78e+00    -  1.00e+00 1.00e+00f  1\n",
      "  11  2.9737858e+02 0.00e+00 1.17e-02  -3.8 2.84e+00    -  1.00e+00 1.00e+00f  1\n",
      "  12  2.9720939e+02 0.00e+00 8.23e-04  -3.8 7.54e-01    -  1.00e+00 1.00e+00f  1\n",
      "  13  2.9654140e+02 0.00e+00 5.71e-03  -5.7 2.14e+00    -  9.85e-01 9.80e-01f  1\n",
      "  14  2.9642932e+02 0.00e+00 1.20e-03  -5.7 9.17e-01    -  1.00e+00 1.00e+00f  1\n",
      "  15  2.9640825e+02 0.00e+00 1.38e-04  -5.7 2.85e-01    -  1.00e+00 1.00e+00f  1\n",
      "  16  2.9640707e+02 0.00e+00 1.80e-05  -5.7 6.18e-02    -  1.00e+00 1.00e+00f  1\n",
      "  17  2.9639711e+02 0.00e+00 8.96e-04  -8.6 1.33e-01    -  1.00e+00 9.40e-01f  1\n",
      "  18  2.9639633e+02 0.00e+00 1.31e-05  -8.6 5.52e-02    -  1.00e+00 1.00e+00f  1\n",
      "  19  2.9639618e+02 0.00e+00 2.63e-06  -8.6 2.47e-02    -  1.00e+00 1.00e+00f  1\n",
      "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
      "  20  2.9639613e+02 0.00e+00 4.04e-07  -8.6 9.68e-03    -  1.00e+00 1.00e+00f  1\n",
      "  21  2.9639612e+02 0.00e+00 1.99e-08  -8.6 2.15e-03    -  1.00e+00 1.00e+00f  1\n",
      "  22  2.9639611e+02 0.00e+00 4.16e-09  -9.0 1.01e-03    -  1.00e+00 1.00e+00f  1\n",
      "\n",
      "Number of Iterations....: 22\n",
      "\n",
      "                                   (scaled)                 (unscaled)\n",
      "Objective...............:   2.9639611355858665e+02    2.9639611355858665e+02\n",
      "Dual infeasibility......:   4.1590235062105567e-09    4.1590235062105567e-09\n",
      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Complementarity.........:   9.5392600612077526e-10    9.5392600612077526e-10\n",
      "Overall NLP error.......:   4.1590235062105567e-09    4.1590235062105567e-09\n",
      "\n",
      "\n",
      "Number of objective function evaluations             = 23\n",
      "Number of objective gradient evaluations             = 23\n",
      "Number of equality constraint evaluations            = 0\n",
      "Number of inequality constraint evaluations          = 23\n",
      "Number of equality constraint Jacobian evaluations   = 0\n",
      "Number of inequality constraint Jacobian evaluations = 23\n",
      "Number of Lagrangian Hessian evaluations             = 22\n",
      "Total CPU secs in IPOPT (w/o function evaluations)   =      0.869\n",
      "Total CPU secs in NLP function evaluations           =      0.766\n",
      "\n",
      "EXIT: Optimal Solution Found.\n",
      "Solver status: Optimal\n",
      "rmsd: 296.39611355858665\n"
     ]
    }
   ],
   "source": [
    "# Solve for the control and state\n",
    "println(\"Solving...\")\n",
    "status = solve(mod)\n",
    "\n",
    "# Display results\n",
    "println(\"Solver status: \", status)\n",
    "println(\"rmsd: \", getobjectivevalue(mod))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now extract the parameters and the peaks, and plot the results. They speak for themself, no need to adopt a complicated process with first constraining peak frequency for instance, as it was necessary to do in Python."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x33a4c9b50>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Amplitude are [100000.0,100000.0,100000.0,100000.0,100000.0];\n",
      "Frequency are [948.864,1019.89,1069.58,1110.0,1170.0];\n",
      "HWHM are [20.0,20.0,20.0,20.0,20.0];\n"
     ]
    }
   ],
   "source": [
    "# parameter extractions\n",
    "amplitudes = getvalue(g_amplitudes)\n",
    "frequency = getvalue(g_frequency)\n",
    "hwhm = getvalue(g_hwhm)\n",
    "\n",
    "model_peaks, peaks = gaussiennes(amplitudes,frequency,hwhm,x) # we construct the model representation and the individual peaks\n",
    "\n",
    "# Doign a new figure\n",
    "figure()\n",
    "\n",
    "#we plot the results\n",
    "plot(x,y,linewidth=2.0,color=\"black\",label=\"Signal to fit\")\n",
    "plot(x,model_peaks,color=\"red\",label=\"Gaussian Model\")\n",
    "\n",
    "for i = 1:5\n",
    "    plot(x,peaks[:,i],color=[1.-i/5.0,1.-i/5.0,i/5.0],label=\"Peak $(i)\")\n",
    "end\n",
    "\n",
    "title(\"Figure 3: the fit of the spectrum\",fontsize = 18, fontweight = \"bold\")\n",
    "\n",
    "# we set the values of the labels of the x and y axis.\n",
    "xlabel(L\"Raman shift, cm$^{-1}$\",fontsize=18) # The L in front of the string indicates that we use Latex for the maths\n",
    "ylabel(\"Intensity, a. u.\",fontsize=18)\n",
    "\n",
    "# we display the legend at the best location, without a frame\n",
    "legend(loc=\"best\",frameon=false)\n",
    "\n",
    "println(\"\")\n",
    "println(\"Amplitude are $(amplitudes);\")\n",
    "println(\"Frequency are $(frequency);\")\n",
    "println(\"HWHM are $(hwhm);\")\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I discussed in the python version of this example that changing the algorithm can give you different results. This is quite true. The good thing is that JuMP just allows you to implement your model. You can change the solver and algorithm without any further difficulty when declaring the model:\n",
    "    mod = Model(solver=IpoptSolver(print_level=0))\n",
    "Very different solvers are available in Julia, and you can choose looking here: http://www.juliaopt.org/\n",
    "\n",
    "Ipopt seems to be pretty good and should fit the needs of most problems quite well. But there is also NLopt, in which you can choose to use the Nelder-Mead algorithm for instance, or Mosek. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Error estimation with bootstrapping\n",
    "\n",
    "Estimation of the errors affecting each parameter will be done with bootstrapping. We first resample with replacement X times our dataset. Then we fit it X times and we save the X parameter sets generated. With X large enough, this provides a robust statistic for the errors on the parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 1, solver status: Optimal\n",
      "Iteration 2, solver status: Optimal\n",
      "Iteration 3, solver status: Optimal\n",
      "Iteration 4, solver status: Optimal\n",
      "Iteration 5, solver status: Optimal\n",
      "Iteration 6, solver status: Optimal\n",
      "Iteration 7, solver status: Optimal\n",
      "Iteration 8, solver status: Optimal\n",
      "Iteration 9, solver status: Optimal\n",
      "Iteration 10, solver status: Optimal\n",
      "Iteration 11, solver status: Optimal\n",
      "Iteration 12, solver status: Optimal\n",
      "Iteration 13, solver status: Optimal\n",
      "Iteration 14, solver status: Optimal\n",
      "Iteration 15, solver status: Optimal\n",
      "Iteration 16, solver status: Optimal\n",
      "Iteration 17, solver status: Optimal\n",
      "Iteration 18, solver status: Optimal\n",
      "Iteration 19, solver status: Optimal\n",
      "Iteration 20, solver status: Optimal\n",
      "Iteration 21, solver status: Optimal\n",
      "Iteration 22, solver status: Optimal\n",
      "Iteration 23, solver status: Optimal\n",
      "Iteration 24, solver status: Optimal\n",
      "Iteration 25, solver status: Optimal\n",
      "Iteration 26, solver status: Optimal\n",
      "Iteration 27, solver status: Optimal\n",
      "Iteration 28, solver status: Optimal\n",
      "Iteration 29, solver status: Optimal\n",
      "Iteration 30, solver status: Optimal\n",
      "Iteration 31, solver status: Optimal\n",
      "Iteration 32, solver status: Optimal\n",
      "Iteration 33, solver status: Optimal\n",
      "Iteration 34, solver status: Optimal\n",
      "Iteration 35, solver status: Optimal\n",
      "Iteration 36, solver status: Optimal\n",
      "Iteration 37, solver status: Optimal\n",
      "Iteration 38, solver status: Optimal\n",
      "Iteration 39, solver status: Optimal\n",
      "Iteration 40, solver status: Optimal\n",
      "Iteration 41, solver status: Optimal\n",
      "Iteration 42, solver status: Optimal\n",
      "Iteration 43, solver status: Optimal\n",
      "Iteration 44, solver status: Optimal\n",
      "Iteration 45, solver status: Optimal\n",
      "Iteration 46, solver status: Optimal\n",
      "Iteration 47, solver status: Optimal\n",
      "Iteration 48, solver status: Optimal\n",
      "Iteration 49, solver status: Optimal\n",
      "Iteration 50, solver status: Optimal\n",
      "Iteration 51, solver status: Optimal\n",
      "Iteration 52, solver status: Optimal\n",
      "Iteration 53, solver status: Optimal\n",
      "Iteration 54, solver status: Optimal\n",
      "Iteration 55, solver status: Optimal\n",
      "Iteration 56, solver status: Optimal\n",
      "Iteration 57, solver status: Optimal\n",
      "Iteration 58, solver status: Optimal\n"
     ]
    },
    {
     "ename": "LoadError",
     "evalue": "LoadError: InterruptException:\nwhile loading In[8], in expression starting on line 5",
     "output_type": "error",
     "traceback": [
      "LoadError: InterruptException:\nwhile loading In[8], in expression starting on line 5",
      "",
      " in getindex(::Tuple{UInt64,Tuple{}}, ::Int64) at ./tuple.jl:8",
      " in toq() at ./util.jl:93",
      " in eval_hesslag(::JuMP.NLPEvaluator, ::Array{Float64,1}, ::Array{Float64,1}, ::Float64, ::Array{Float64,1}) at /Users/charles/.julia/v0.5/JuMP/src/nlp.jl:800",
      " in (::Ipopt.#eval_h_cb#8{JuMP.NLPEvaluator})(::Array{Float64,1}, ::Symbol, ::Array{Int32,1}, ::Array{Int32,1}, ::Float64, ::Array{Float64,1}, ::Array{Float64,1}) at /Users/charles/.julia/v0.5/Ipopt/src/IpoptSolverInterface.jl:86",
      " in eval_h_wrapper(::Int32, ::Ptr{Float64}, ::Int32, ::Float64, ::Int32, ::Ptr{Float64}, ::Int32, ::Int32, ::Ptr{Int32}, ::Ptr{Int32}, ::Ptr{Float64}, ::Ptr{Void}) at /Users/charles/.julia/v0.5/Ipopt/src/Ipopt.jl:149",
      " in solveProblem(::Ipopt.IpoptProblem) at /Users/charles/.julia/v0.5/Ipopt/src/Ipopt.jl:304",
      " in optimize!(::Ipopt.IpoptMathProgModel) at /Users/charles/.julia/v0.5/Ipopt/src/IpoptSolverInterface.jl:122",
      " in #solvenlp#139(::Bool, ::Function, ::JuMP.Model, ::JuMP.ProblemTraits) at /Users/charles/.julia/v0.5/JuMP/src/nlp.jl:1246",
      " in (::JuMP.#kw##solvenlp)(::Array{Any,1}, ::JuMP.#solvenlp, ::JuMP.Model, ::JuMP.ProblemTraits) at ./<missing>:0",
      " in #solve#88(::Bool, ::Bool, ::Bool, ::Array{Any,1}, ::Function, ::JuMP.Model) at /Users/charles/.julia/v0.5/JuMP/src/solvers.jl:138",
      " in macro expansion; at ./In[8]:27 [inlined]",
      " in anonymous at ./<missing>:?"
     ]
    }
   ],
   "source": [
    "nb_boot = 500 # times we do the bootstrap\n",
    "\n",
    "params_boot = Array{Float64}(nb_boot,3,m) # for recording the parameters generated\n",
    "\n",
    "for k = 1:nb_boot # bootstrapping loop\n",
    "    \n",
    "    b_x_f, b_y_f = bootsample(x,y) #resampling data with replacement with Spectra.jl bootsample function\n",
    "    \n",
    "    mod2 = Model(solver=IpoptSolver(print_level=0))# The model for fitting baseline to roi signal\n",
    "    \n",
    "    @variable(mod2,g_amplitudes_b[i=1:m] >= 0.0)\n",
    "    @variable(mod2,850 <= g_frequency_b[i=1:m] <= 1250)\n",
    "    @variable(mod2,20.0 <= g_hwhm_b[i=1:m] <= 50.0)\n",
    "\n",
    "    # setting initial values\n",
    "    setvalue(g_amplitudes_b[i=1:m],amp_guess[i])\n",
    "    setvalue(g_frequency_b[i=1:m],freq_guess[i])\n",
    "    setvalue(g_hwhm_b[i=1:m],hwhm_guess[i])\n",
    "    \n",
    "    # set bounds\n",
    "    setupperbound(g_hwhm_b[3], 35.0 )\n",
    "    setupperbound(g_hwhm_b[5], 45.0 )\n",
    "    setlowerbound(g_frequency_b[5], 1160 )\n",
    "    \n",
    "    @NLexpression(mod2,g_mod_b[j=1:n],sum{g_amplitudes_b[i] *exp(-log(2) * ((b_x_f[j]-g_frequency_b[i])/g_hwhm_b[i])^2), i = 1:m})\n",
    "    @NLobjective(mod2,Min,sum{(g_mod_b[j] - b_y_f[j])^2, j=1:n})\n",
    "    status = solve(mod2)\n",
    "    println(\"Iteration $(k), solver status: \", status) # we print some information\n",
    "    \n",
    "    params_boot[k,1,:] = getvalue(g_amplitudes_b)\n",
    "    params_boot[k,2,:] = getvalue(g_frequency_b)\n",
    "    params_boot[k,3,:] = getvalue(g_hwhm_b)\n",
    "end\n",
    "        \n",
    "\n",
    "println(\"Done...\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When this is done, we can save the params_boot matrix somewhere in the system with JLD, the Julia file format. We could also output it as a .txt file to use it with another program for plotting, for instance. Then we construct an array that looks at how the parameter std changes during the bootstrapping, for instance. When stable, we can assume we did enought bootstrap to get good results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "From bootstrapping, values for peak 1:\n",
      "Amplitude mean [NaN] and std [NaN]\n",
      "Raman shift (cm^{-1}) mean [NaN] and std [NaN]\n",
      "HWHM (cm^{-1}) mean [3.01323] and std [8.32704]\n",
      "\n",
      "From bootstrapping, values for peak 2:\n",
      "Amplitude mean [0.884591] and std [7.05225]\n",
      "Raman shift (cm^{-1}) mean [122.516] and std [336.004]\n",
      "HWHM (cm^{-1}) mean [6.63999] and std [17.2309]\n",
      "\n",
      "From bootstrapping, values for peak 3:\n",
      "Amplitude mean [0.897944] and std [7.05141]\n",
      "Raman shift (cm^{-1}) mean [NaN] and std [NaN]\n",
      "HWHM (cm^{-1}) mean [NaN] and std [NaN]\n",
      "\n",
      "From bootstrapping, values for peak 4:\n",
      "Amplitude mean [NaN] and std [NaN]\n",
      "Raman shift (cm^{-1}) mean [NaN] and std [NaN]\n",
      "HWHM (cm^{-1}) mean [7.56229] and std [10.3095]\n",
      "\n",
      "From bootstrapping, values for peak 5:\n",
      "Amplitude mean [4.39389] and std [6.81913]\n",
      "Raman shift (cm^{-1}) mean [141.331] and std [374.48]\n",
      "HWHM (cm^{-1}) mean [10.4473] and std [14.247]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "save(\"./data/Raman_fitting_example_results.jld\",\"params_boot\",params_boot) # saving the results with JLD\n",
    "\n",
    "for i = 1:m\n",
    "    println(\"From bootstrapping, values for peak $(i):\")\n",
    "    println(\"Amplitude mean $(mean(params_boot[:,1,i],1)) and std $(std(params_boot[:,1,i],1))\")\n",
    "    println(\"Raman shift (cm^{-1}) mean $(mean(params_boot[:,2,i],1)) and std $(std(params_boot[:,2,i],1))\")\n",
    "    println(\"HWHM (cm^{-1}) mean $(mean(params_boot[:,3,i],1)) and std $(std(params_boot[:,3,i],1))\")\n",
    "    println(\"\")\n",
    "end\n",
    "\n",
    "# bootstrap performance recorder\n",
    "bootrecord = zeros(nb_boot,3,m)\n",
    "for k = 1:nb_boot\n",
    "    for j = 1:3\n",
    "    for i = 1:m\n",
    "        if k == 1\n",
    "            bootrecord[k,j,i] = 0.0\n",
    "        else\n",
    "            bootrecord[k,j,i] = sum(std(params_boot[1:k,j,i],1))\n",
    "        end\n",
    "    end\n",
    "    end\n",
    "end"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now look at the bootstrapping results. param_interest_boot_stat allows you to choose the parameter you want to look at (1 for amplitude, 2 for frequency, 3 for HWHM), and peak_interest_boot_stat the peak you want to look at."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "LoadError",
     "evalue": "LoadError: PyError (:PyObject_Call) <type 'exceptions.AttributeError'>\nAttributeError('max must be larger than min in range parameter.',)\n  File \"/Users/charles/anaconda/lib/python2.7/site-packages/matplotlib/pyplot.py\", line 2958, in hist\n    stacked=stacked, data=data, **kwargs)\n  File \"/Users/charles/anaconda/lib/python2.7/site-packages/matplotlib/__init__.py\", line 1812, in inner\n    return func(ax, *args, **kwargs)\n  File \"/Users/charles/anaconda/lib/python2.7/site-packages/matplotlib/axes/_axes.py\", line 6010, in hist\n    m, bins = np.histogram(x[i], bins, weights=w[i], **hist_kwargs)\n  File \"/Users/charles/anaconda/lib/python2.7/site-packages/numpy/lib/function_base.py\", line 176, in histogram\n    'max must be larger than min in range parameter.')\n\nwhile loading In[10], in expression starting on line 15",
     "output_type": "error",
     "traceback": [
      "LoadError: PyError (:PyObject_Call) <type 'exceptions.AttributeError'>\nAttributeError('max must be larger than min in range parameter.',)\n  File \"/Users/charles/anaconda/lib/python2.7/site-packages/matplotlib/pyplot.py\", line 2958, in hist\n    stacked=stacked, data=data, **kwargs)\n  File \"/Users/charles/anaconda/lib/python2.7/site-packages/matplotlib/__init__.py\", line 1812, in inner\n    return func(ax, *args, **kwargs)\n  File \"/Users/charles/anaconda/lib/python2.7/site-packages/matplotlib/axes/_axes.py\", line 6010, in hist\n    m, bins = np.histogram(x[i], bins, weights=w[i], **hist_kwargs)\n  File \"/Users/charles/anaconda/lib/python2.7/site-packages/numpy/lib/function_base.py\", line 176, in histogram\n    'max must be larger than min in range parameter.')\n\nwhile loading In[10], in expression starting on line 15",
      "",
      " in pyerr_check at /Users/charles/.julia/v0.5/PyCall/src/exception.jl:56 [inlined]",
      " in pyerr_check at /Users/charles/.julia/v0.5/PyCall/src/exception.jl:61 [inlined]",
      " in macro expansion at /Users/charles/.julia/v0.5/PyCall/src/exception.jl:81 [inlined]",
      " in #_pycall#62(::Array{Any,1}, ::Function, ::PyCall.PyObject, ::Array{Float64,1}, ::Vararg{Any,N}) at /Users/charles/.julia/v0.5/PyCall/src/PyCall.jl:546",
      " in _pycall(::PyCall.PyObject, ::Array{Float64,1}, ::Vararg{Any,N}) at /Users/charles/.julia/v0.5/PyCall/src/PyCall.jl:534",
      " in #pycall#66(::Array{Any,1}, ::Function, ::PyCall.PyObject, ::Type{PyCall.PyAny}, ::Array{Float64,1}, ::Vararg{Any,N}) at /Users/charles/.julia/v0.5/PyCall/src/PyCall.jl:568",
      " in pycall(::PyCall.PyObject, ::Type{PyCall.PyAny}, ::Array{Float64,1}, ::Vararg{Any,N}) at /Users/charles/.julia/v0.5/PyCall/src/PyCall.jl:568",
      " in #call#67(::Array{Any,1}, ::PyCall.PyObject, ::Array{Float64,1}, ::Vararg{Any,N}) at /Users/charles/.julia/v0.5/PyCall/src/PyCall.jl:571",
      " in (::PyCall.PyObject)(::Array{Float64,1}, ::Vararg{Any,N}) at /Users/charles/.julia/v0.5/PyCall/src/PyCall.jl:571"
     ]
    }
   ],
   "source": [
    "# bootstrap performance\n",
    "param_interest_boot_stat =2\n",
    "peak_interest_boot_stat = 3\n",
    "\n",
    "# Graphics\n",
    "fig = figure()\n",
    "plot(1:nb_boot, bootrecord[:,param_interest_boot_stat,peak_interest_boot_stat],label=\"SOI\")\n",
    "\n",
    "title(\"Figure 4: Standard deviation\",fontsize = 18, fontweight = \"bold\")\n",
    "\n",
    "xlabel(\"Number of iterations during bootstrap\",fontsize=18)\n",
    "ylabel(\"Parameter $(param_interest_boot_stat), peak $(peak_interest_boot_stat)\",fontsize=18)\n",
    "\n",
    "fi2= figure()\n",
    "h = plt[:hist](params_boot[:,param_interest_boot_stat,peak_interest_boot_stat],100)\n",
    "\n",
    "title(\"Figure 5: Average value\",fontsize = 18, fontweight = \"bold\")\n",
    "\n",
    "xlabel(\"Parameter value\",fontsize=18)\n",
    "ylabel(\"Parameter $(param_interest_boot_stat), peak $(peak_interest_boot_stat)\",fontsize=18)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
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